Blockchain technology and gender equality: A systematic literature review

2023 | International Journal of Information Management | Citations: 0

Authors: Di Vaio, Assunta; Hassan, Rohail; Palladino, Rosa

Abstract: This study examines the literary corpus on the role and potential of blockchain ... Expand

Abstract: This study examines the literary corpus on the role and potential of blockchain technology in promoting gender equality through the lens of new technology-oriented corporate governance models. It investigates if and how corporate governance models can include blockchain technology to add value to gender equality and inclusion processes, in line with Sustainable Development Goal (SDG) 5. A bibliometric analysis of a database—containing 127 articles, 4 United Nations reports, 3 European institutions’ reports, 1 International Labour Office report, 1 World Economic Forum report, and 4 industry reports useful to our analysis—was conducted from 1990 to 2021, to provide a map of the knowledge generated and circulated by the literature. This study offers insights into publication activities, essential topics, citation trends, and the status of collaborations between contributors to previous research and aggregated contributions to the area of blockchain technology studies. Furthermore, the study offers a retrospective analysis of the content published in the blockchain technology field. The findings indicate that field research has focussed primarily on blockchain’s economic and financial attributes but not on social potential. This study emphasises the implementation of blockchain technology to manage gender equality and inclusion processes by orienting corporate governance models towards social and sustainable values. Collapse

Topics: blockchain database system information system use corporate governance business process management

Methods: bibliometric analysis literature sample qualitative content analysis VOSviewer literature study

Theories: institutional theory

MaCuDE IS Task Force Phase II Report: Views of Industry Leaders on Big Data Analytics and AI

2023 | Communications of the Association for Information Systems | Citations: 2

Authors: Lyytinen, Kalle; Topi, Heikki; Tang, Jing

Abstract: This paper represents the Phase II report of the Management Curriculum for the ... Expand

Abstract: This paper represents the Phase II report of the Management Curriculum for the Digital Era (MaCuDE) disciplinary task force on information systems (IS). Aligned with the current work of the AIS (Association for Information Systems) and ACM (Association for Computing Machinery), we focus on the current and future industry driven educational needs and requirements posed by big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and related innovations. In this report, we probe and report on the views of industry leaders regarding BDA/AI education needs. We conducted 18 rich semi-structured interviews with a representative sample of industry leaders situated around key changes and issues related to workforce demands in digital transformation and associated educational needs. We performed a grounded theory based analysis of key themes in reported education needs. We note the shifting meaning of AI and BDA phenomena and identify three main organizational level needs for the digital eracapability improvement and transformation, decision-making strategies and tactics, and changes in operations or products -and connect them to three individual professional competencies necessary to deliver them: fundamental environmental competencies, data information and content, and system design competencies. Based on the analysis we outline several novel competency-based IS curriculum recommendations for the master's and undergraduate level IS education.Architecting for cloud: "And so the thing I noticed, a lot of these companies, when their data is growing so big and their database costs are running so high, they think about going towards Open Source Software, Open Source databases to store all the data and, hence, that creates a demand on the workforce to actually know how to use those. "Understanding data structures, architectures, and governance: "So that's his role, and the key requirement for that role again is Data Architecture, data Engineering and Data Management" Building and managing machine learning models: "Second, of course, is strong competency in the use of Machine Learning packages or models. This field is evolving aggressively." "For developing or programming our own AI solutions, our employees have no deep knowledge of mathematical models and how to build mature and value-creating AI applications." IS management and operations: "The talent sort of wars out there are really, really tough, and finding people who understand that, (how to) generate the data, use the data, think about the data, manipulate it are really, really important. I agree. It used to be you had to have people with Project Management. That was critical and that was everyone competed for that, and now it's that plus the data, the Machine Learning."Based on the data collected in this study, we cannot offer a clear answer to the questions of how BDA and AI affect areas that do not belong to the two main areas of the study. Given, however, how quickly BDA and AI have grown and how substantial related resource requirements are in organizations, it is clear that they already have an impact on several competency needs such as programming language selection, selection of BDA and AI tools based on cloud-based solutions, data architecture, data governance, service repertoires available on platform environments, and special characteristic of BDA and AI projects and how they affect education for project management needs and competencies. Summary of Findings Related to Individual CompetenciesGenerally, we observe a strong alignment and continuity between the recent information systems curriculum recommendations and the findings of this study. The data highlights the continued importance of IS core topics covered in all past curricula, such as data management, systems development, IT infrastructure and architecture (including security), and IS (project) management.The results also highlight also multiple areas that need more emphasis and careful design decisions when schools plan and implement their BDA and AI based programs: Integration of business and technology competencies in ways that allow seamless communication with both business and technology experts  A stronger emphasis on multiple life cycle models and concepts related to data management and data science than in the recent model curricula focused on system life cycles  Heightened focus on new dimensions of data analytics competencies (including communication-related elements such a storytelling and visualization)  The growing importance of ethics as a foundation for ensuring privacy and security of BDA and AI models  Need for more skills in statistics  Understanding the novel requirements that AI solutions set up for IT infrastructure  The importance of competencies in AI systems development and deployment. Collapse

Topics: artificial intelligence big data analytical information system data management decision making

Methods: qualitative interview machine learning meta learning field study personal interview

Champions for Social Good: How Can We Discover Social Sentiment and Attitude-Driven Patterns in Prosocial Communication?

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Mukkamala, Raghava; Kauffman, Robert; Henriksen, Helle

Abstract: The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strateg ... Expand

Abstract: The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strategy to inform people about displaced populations and refugee emergencies. It is actively engaging public figures to increase awareness through its prosocial communications and improve social informedness and support for policy changes in its services. We studied the Twitter communications of UNHCR social media champions and investigated their role as high-profile influencers. In this study, we offer a design science research and data analytics framework and propositions based on the social informedness theory we propose in this paper to assess communication about UNHCR's mission. Two variables-refugee-emergency and champion type-relate to the informedness of UNHCR champions' followers. Based on a Twitter sentiment and attitude corpus, we applied a five-step design science analytics framework involving machine learning and natural language processing to test how the emergency type and champion type impact social communication patterns. Positive and neutral sentiment dominated the tweets of the champions and their followers for most refugee-emergency types. High participation-intensity champions emphasized high-intensity emergencies with tweet patterns reflecting dominant positive or neutral sentiment and sharing/liking attitudes. However, we found that sports figures effects were limited in spreading UNHCR's message, despite their millions of followers. We demonstrate the power of data science for prosocial policy based on refugee crisis awareness and instantiate our methods and knowledge contributions in a research framework that derives knowledge, decisions, and actions from behavioral, design, and economics of information systems perspectives. Collapse

Topics: Twitter social media analytical information system big data application programming interface

Methods: design science BERT natural language processing unsupervised learning artificial neural network

Theories: Lewin's field theory

Improving scale adaptation practices in information systems research: Development and validation of a cognitive validity assessment method

2023 | Information Systems Journal | Citations: 1

Authors: Pillet, Jean-Charles; Carillo, Kevin D.; Vitari, Claudio; Pigni, Federico

Abstract: Scale adaptation, where authors alter the wording of an already published scale, ... Expand

Abstract: Scale adaptation, where authors alter the wording of an already published scale, is a deeply rooted social practice in IS research. This paper argues that the time is ripe to question this activity as well as the beliefs that have progressively formed around it. We identify and challenge five fallacious scale adaptation beliefs that hinder the development of more robust measure development norms. Contributing to this area of research, this paper offers a conceptual definition of the cognitive validity concept, defined as the extent to which a scale is free of problematic item characteristics (PICs) that bias the survey response process and subsequent empirical results. Building on this conceptualization effort, a new methodological process for assessing the cognitive validity of adapted IS measures is introduced. Through a series of three programmatic studies, we find converging evidence that the method can benefit the IS field by making the scale adaptation process more robust, transparent, and consistent. Along with the method, we introduce a new index that IS scholars can use to benchmark the cognitive quality of their scales against venerable IS measures. We discuss the implications of our work for IS research (including detailed implementation guidelines) and provide directions for future research on measurement in IS. Collapse

Topics: crowdsourcing privacy website evaluation criteria decision making

Methods: survey F-test parametric test literature study statistical hypothesis test

Bringing Educational Chatbots into the Field: Towards a Semi-Automated Knowledge Base Generation App for Educational Chatbots

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Hobert, Sebastian

Abstract: The overall goal of this design-oriented research study is to foster the dissem ... Expand

Abstract: The overall goal of this design-oriented research study is to foster the dissemination of educational chatbots. Even though prior research has shown that educational chatbots can have positive effects on learning and teaching, we observe that the technology is currently only used rarely. Lately, technical challenges in creating educational chatbots have been reduced. Nevertheless, a major challenge remains: the availability of tailored learning materials stored in knowledge bases. Such well-design knowledge bases are currently needed for educational chatbots that are specifically designed for courses. To support educators in creating knowledge bases, we propose a semi-automated knowledge base generation process and develop a corresponding app that uses techniques from text mining. In our evaluation, we compared the results of the semi-automated approach with a manually created knowledge base from a prior study. The results indicate that the semi-automated approach is able to provide an efficient way to create a knowledge base. Collapse

Topics: knowledge base mobile application PHP usability user interface

Methods: chatbot natural language processing design science design artifact design process

Formation and Action of a Learning Community with Collaborative Learning Software

2023 | Journal of Management Information Systems | Citations: 0

Authors: Eryilmaz, Evren; Thoms, Brian; Ahmed, Zafor; Lee, Howard

Abstract: This paper explores the formation of a learning community facilitated by custom ... Expand

Abstract: This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model. Collapse

Topics: recommender system collaborative filtering pandemic online learning system open source

Methods: qualitative content analysis descriptive statistic sociometry synthesis mixed method

Theories: cognitive theory

Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Adamopoulos, Panagiotis; Ghose, Anindya; Tuzhilin, Alexander

Abstract: In this paper, we examine the effectiveness of various recommendation strategies ... Expand

Abstract: In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers’ utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. Interestingly, recommendation strategies that directly embed social proofs for the recommended alternatives outperform other recommendations. In addition, recommendation strategies combining social proofs with higher levels of induced awareness due to the prescribed temporal diversity have an even stronger effect on the mobile channel. We also examine the heterogeneity of the demand effect across items, users, and contextual settings, further verifying empirically the aforementioned information and persuasion mechanisms and generating rich insights. We also facilitate the estimation of causal effects in the presence of endogeneity using machine-learning methods. Specifically, we develop novel econometric instruments that capture product differentiation (isolation) based on deeplearning models of user-generated reviews. Our empirical findings extend the current knowledge regarding the heterogeneous impact of recommender systems, reconcile contradictory prior results in the related literature, and have significant business implications. Collapse

Topics: mobile system broadband cellular network mobile channel online review mobile application

Methods: econometric modeling instrumental variables estimation machine learning robustness check computational algorithm

Theories: symbolic interaction theory social network theory cognitive theory consumer search theory trust theory

Information technology in open innovation: A resource orchestration perspective

2022 | Information & Management | Citations: 4

Authors: Cui, Tingru; Ye, Jonathan Hua; Tan, Chuan Hoo

Abstract: Building on the resource orchestration view, we proposed a dual role of informat ... Expand

Abstract: Building on the resource orchestration view, we proposed a dual role of information technology (IT) in the form of IT-enabled resource acquisition (IRA) and IT-enabled resource operation (IRO) in determining innovation performance. Through analyzing the survey data from 235 organizations, we found that in a dynamic business environment, IRA assists organizations in accruing benefits from their open innovation (OI) strategies. In a less dynamic business environment, IRO enhances the impact of OI strategies on innovation performance, whereas IRA diminishes such an impact. Post-hoc interviews reveal further insights into how organizations utilize ITs (IRA and IRO) to obtain innovation benefits. Collapse

Topics: innovation management organizational innovation research and development information system use information technology capability

Methods: qualitative interview survey statistical hypothesis test machine learning field study

Cluster Analysis in Online Learning Communities: A Text Mining Approach

2022 | Communications of the Association for Information Systems | Citations: 6

Authors: Eryilmaz, Evren; Thoms, Brian; Ahmed, Zafor

Abstract: This paper presents a theory-informed blueprint for mining unstructured text da ... Expand

Abstract: This paper presents a theory-informed blueprint for mining unstructured text data using mixed-and multi-methods to improve understanding of collaboration in asynchronous online discussions (AOD). Grounded in a community of inquiry theoretical framework to systematically combine established research techniques, we investigated how AOD topics and individual reflections on those topics affect the formation of clusters or groups in a community. The data for the investigation came from 54 participants and 470 messages. Data analysis combined the analytical efficiency and scalability of topic modeling, social network analysis, and cluster analysis with qualitative content analysis. The cluster analysis found three clusters and that members of the intermediate cluster (i.e., middle of three clusters) played a pivotal role in this community by expressing uncertainty statements, which facilitated a collective sense-making process to resolve misunderstandings. Furthermore, we found that participants' selected discussion topics and how they discussed those topics influenced cluster formations. Theoretical, practical, and methodological implications are discussed in depth. Collapse

Topics: social network online learning system open source social presence big data

Methods: topic model mixed method cluster analysis sociometry qualitative content analysis

Instant Messaging and Employee's Performance: A Text Mining Approach

2022 | International Conference on Information Systems | Citations: 0

Authors: Esmaeili, Mostafa; Zantedeschi, Daniel

Abstract: The adoption of Instant Messaging (IM) applications in the workplace remains co ... Expand

Abstract: The adoption of Instant Messaging (IM) applications in the workplace remains contentious due to difficulties in adequately quantifying organizational benefits and how it affects individual performance. Previous research on the impact of IM usage on employee performance has been limited to analyzing primary data (i.e., survey methods), making it difficult to extrapolate the findings to a constantly changing workplace. In contrast, we investigate the relationships between these individuals' IM usage at the workplace and their primary assessment metric in their organization, performance evaluation, using longitudinal data of employees' IM activities and their performance evaluation collected from a US Fortune 500 financial company. Using cutting-edge text-mining techniques, we identify the primary purposes of IM utilization in organizations and assess the impact of those attributes on employee performance. Our findings show that IM in the workplace can improve team communication, knowledge-sharing experience, and social networking among employees, but it can also be disruptive. However, the combined effect of team communication and knowledge sharing on employee performance can overshadow the negative impact of IM interruption on employee performance. Collapse

Topics: individual productivity knowledge sharing social network information system use Python

Methods: regression analysis method natural language processing hierarchical linear modeling longitudinal research random forest classification